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Accurately predicting industrial electricity consumption is of paramount importance for optimizing energy management and operational efficiency. Traditional forecasting approaches face significant challenges in capturing the complex factors influencing industrial electricity consumption, often due to the inadequate representation of correlations, thus limiting their predictive capabilities. To overcome these limitations, we propose a novel graph-based forecasting model termed Industry–Geography Time Series Forecasting Model (IG-TFM). Our approach leverages historical electricity consumption data and geographical information relevant to similar industries to construct an industry–geography relationship graph. This graph serves as the foundation of a comprehensive network that encompasses all industries of interest, allowing us to identify sectors closely associated with the target industry. The structured graph data are then processed within a graph convolutional neural network framework, which effectively captures the impact of geographical location, industry similarities, and inter-industry relationships on electricity consumption patterns. Utilizing this enriched representation, we develop our IG-TFM for accurate time series forecasting of industrial electricity consumption. Experiments conducted on real-world data, including 31 industries across 9 cities in a southern province of China, demonstrate the significant advantages of our proposed method across key performance indicators such as the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). These findings underscore the importance and efficacy of employing complex networks to encode sequence-related information, thereby substantially improving prediction accuracy in industrial electricity consumption forecasting.
Zhan et al. (Wed,) studied this question.